{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import numpy as np\n",
    "\n",
    "##解决图例中文乱码，设置参数\n",
    "matplotlib.rcParams['font.family']=['sans-serif']\n",
    "matplotlib.rcParams['font.sans-serif']=['Songti SC']#显示中文\n",
    "matplotlib.rcParams['font.serif']=['Songti SC']\n",
    "matplotlib.rcParams['axes.unicode_minus']=False #正常显示符号\n",
    "from math import pi\n",
    "\n",
    "#屏蔽警告信息\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基本雷达图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "'group': ['A','B','C','D'],\n",
    "'var1': [38, 1.5, 30, 4],\n",
    "'var2': [29, 10, 9, 34],\n",
    "'var3': [8, 39, 23, 24],\n",
    "'var4': [7, 31, 33, 14],\n",
    "'var5': [28, 15, 32, 14]\n",
    "})\n",
    "\n",
    "categories=list(df)[1:]  #['var1', 'var2', 'var3', 'var4', 'var5']\n",
    "N = len(categories)\n",
    "\n",
    "values=df.loc[0].drop('group').values.flatten().tolist()\n",
    "values += values[:1] #[38.0, 29, 8, 7, 28, 38.0]\n",
    "\n",
    "angles = [n / float(N) * 2 * pi for n in range(N)]\n",
    "angles += angles[:1]\n",
    "\n",
    "#填充饼图的刻度\n",
    "ax = plt.subplot(111, polar=True)\n",
    "plt.xticks(angles[:-1], categories, color='grey', size=8)\n",
    "\n",
    "#设置Y轴刻度\n",
    "ax.set_rlabel_position(0)\n",
    "plt.yticks([10,20,30], [\"10\",\"20\",\"30\"], color=\"grey\", size=7)\n",
    "plt.ylim(0,40)\n",
    "\n",
    "\n",
    "# 填充数据\n",
    "ax.plot(angles, values, linewidth=1, linestyle='solid')\n",
    "# 阴影面积填充\n",
    "ax.fill(angles, values, 'b', alpha=0.1)\n",
    "# Show the graph\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
